[None][feat] Add support for KVCache reuse for DSv32

cpp/include/tensorrt_llm/batch_manager/kvCacheUtils.h

@@ -183,6 +183,10 @@ class BlockRange
             auto windowSize = cacheManager.getBlockManager().getPoolWindowSize(poolIdx);
             mPoolsPerWindow[windowSize].push_back(cacheManager.getBlockManager().getPrimaryPool(poolIdx));
         }
+        if (cacheManager.isEnableIndexerKCache())
+        {
+            mIndexerKCachePool = cacheManager.getIndexerKCachePool();
+        }
     }
 
     BlockRange(BaseKVCacheManager const& cacheManager, LlmRequest::RequestIdType requestId)

cpp/tensorrt_llm/batch_manager/dataTransceiver.cpp

@@ -806,7 +806,7 @@ class CacheReceiver::Impl
 
         RequestInfo requestInfo(requestId, mSelfState);
 
-        if (mFormatter->getCacheManager()->getBlockManager().getNumPools() == 1)
+        if (!mFormatter->getCacheManager()->getBlockManager().isVariableWindow())
         {
             auto* cacheManager = mFormatter->getCacheManager();
             auto beam = 0;

cpp/tensorrt_llm/batch_manager/kvCacheManager.cpp

@@ -876,14 +876,7 @@ void WindowBlockManager::allocatePools(bool useUvm)
         }
 
         nvinfer1::Dims cacheShape;
-        if (pool.containsIndexerKCache)
-        {
-            cacheShape = ITensor::makeShape({mNumPrimaryBlocks, pool.numLayers, blockSize});
-        }
-        else
-        {
-            cacheShape = ITensor::makeShape({mNumPrimaryBlocks, pool.numLayers, mKVFactor, blockSize});
-        }
+        cacheShape = ITensor::makeShape({mNumPrimaryBlocks, pool.numLayers, mKVFactor, blockSize});
 
         TLLM_LOG_DEBUG("[%s] Allocating primary pool with %d blocks for %d layers with %d kv heads", mLogPrefix.c_str(),
             mNumPrimaryBlocks, pool.numLayers, pool.numKvHeads);

examples/models/core/deepseek_v3/README.md

@@ -881,12 +881,3 @@ python quickstart_advanced.py --model_dir <YOUR_MODEL_DIR> --enable_chunked_pref
 - **GPU Memory:** Adjust `--max_batch_size` and `--max_num_tokens` if you encounter out-of-memory errors.
 - **Logs:** Check `/workspace/trt_bench.log` for detailed performance information and troubleshooting messages.
 - **Configuration Files:** Verify that the configuration files are correctly formatted to avoid runtime issues.
-
-## Known Issues
-- Support for KV Cache Reuse and Chunked Prefill in DeepSeek-V3.2-Exp is currently under development. When running `quickstart_advanced.py`, please include `--disable_kv_cache_reuse` to disable KV Cache Reuse. When using `trtllm-eval`/`trtllm-serve`/`trtllm-bench`, please include the following configuration in the extra llm_api options:
-```
-kv_cache_config:
-    enable_block_reuse: false
-    tokens_per_block: 64
-enable_chunked_prefill: false
-```

tensorrt_llm/_torch/attention_backend/sparse/dsa.py

@@ -930,15 +930,15 @@ def prepare(metadata: DSAtrtllmAttentionMetadata):
                     start_idx=0,
                 )
 
-                if len(chunk_groups) > 1:
+                if len(chunk_groups
+                       ) > 1 or metadata.enable_context_mla_with_cached_kv:
                     metadata.indexer_prefill_chunks = [
                         Indexer.prepare_one_prefill_chunk(
                             metadata,
                             chunk_specs,
                         ) for chunk_specs in chunk_groups
                     ]
                 else:
-                    # Single chunk - use non-chunked fallback path
                     metadata.indexer_prefill_chunks = None
 
             host_cu_seqlen_ks, host_cu_seqlen_ke = compute_cu_seqlen_kv_bounds_with_cache(
@@ -1018,9 +1018,9 @@ def prepare(metadata: DSAtrtllmAttentionMetadata):
         metadata.slot_mapping_scale[:total_tokens].copy_(
             metadata.host_slot_mapping_scale[:total_tokens], non_blocking=True)
 
-        # Only when MLA chunked prefill is enabled, we need to gather the full KV for indexer's logit computation.
+        # When chunked prefill or KVCache reuse is enabled, we need to gather the full KV for indexer's logit computation.
         # Indexer's own chunking does not need full KV gathering, instead it gathers only the current chunk with loop-based gathering.
-        _need_full_kv_gathering = num_contexts > 0 and has_mla_chunked_prefill
+        _need_full_kv_gathering = num_contexts > 0 and metadata.enable_context_mla_with_cached_kv
         if _need_full_kv_gathering:
             total_kv_len = metadata.host_ctx_kv_indptr[num_contexts].item()
             total_kv_per_request = seq_lens[:
@@ -1589,10 +1589,6 @@ def __init__(
         sparse_attn_config: "SparseAttentionConfig",
         **kwargs,
     ) -> None:
-
-        if kv_cache_config.enable_block_reuse:
-            raise NotImplementedError(
-                "DSA indexer K-cache manager does not support block reuse yet")
         self.quant_block_size = 128
         self.index_head_dim = sparse_attn_config.index_head_dim
         # Use a fixed tokens_per_block for indexer k cache due to DG kernel constraints

tests/integration/defs/accuracy/test_disaggregated_serving.py

@@ -1055,7 +1055,6 @@ def test_auto_dtype(self, overlap_scheduler):
         ctx_server_config["cache_transceiver_config"] = {"backend": "DEFAULT"}
         gen_server_config["cache_transceiver_config"] = {"backend": "DEFAULT"}
         ctx_server_config["kv_cache_config"] = {
-            "enable_block_reuse": False,
             "free_gpu_memory_fraction": 0.7,
             "tokens_per_block": 64,
             "dtype": "fp8"
@@ -1072,7 +1071,6 @@ def test_auto_dtype(self, overlap_scheduler):
         ctx_server_config["enable_attention_dp"] = True
         ctx_server_config["enable_autotuner"] = False
         gen_server_config["kv_cache_config"] = {
-            "enable_block_reuse": False,
             "tokens_per_block": 64,
             "free_gpu_memory_fraction": 0.7,
             "dtype": "fp8"

tests/integration/defs/accuracy/test_llm_api_pytorch.py

@@ -2597,17 +2597,13 @@ def test_fp8_blockscale(self, tp_size, pp_size, ep_size, mtp_nextn, fp8kv,
         if get_sm_version() == 100 or get_sm_version() == 103:
             moe_backend = "DEEPGEMM" if moe_backend == "_DEFAULT" else moe_backend
             moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384)
-            # TODO: Support block reuse for DeepSeek-V3.2
-            kv_cache_config = KvCacheConfig(enable_block_reuse=False,
-                                            free_gpu_memory_fraction=0.6,
+            kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.6,
                                             tokens_per_block=64)
         else:
             if moe_backend != "_DEFAULT":
                 pytest.skip("Not supported MoE backend!")
             moe_config = MoeConfig()
-            # TODO: Support block reuse for DeepSeek-V3.2
-            kv_cache_config = KvCacheConfig(enable_block_reuse=False,
-                                            free_gpu_memory_fraction=0.7,
+            kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.7,
                                             tokens_per_block=64)
 
         pytorch_config = dict(
@@ -2670,8 +2666,7 @@ def test_nvfp4_multi_gpus(self, tp_size, pp_size, ep_size, mtp_nextn, fp8kv,
                 "MOE TRTLLM backend does not support SM version 120 or 121")
 
         moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384)
-        kv_cache_config = KvCacheConfig(enable_block_reuse=False,
-                                        free_gpu_memory_fraction=0.7,
+        kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.7,
                                         tokens_per_block=64)
         cuda_graph_config = CudaGraphConfig(
             enable_padding=True,
@@ -2730,8 +2725,7 @@ def test_nvfp4_multi_gpus_chunked_prefill(self, tp_size, pp_size, ep_size,
                 "MOE TRTLLM backend does not support SM version 120 or 121")
 
         moe_config = MoeConfig(backend=moe_backend, max_num_tokens=16384)
-        kv_cache_config = KvCacheConfig(enable_block_reuse=False,
-                                        free_gpu_memory_fraction=0.7,
+        kv_cache_config = KvCacheConfig(free_gpu_memory_fraction=0.7,
                                         tokens_per_block=64)
         cuda_graph_config = CudaGraphConfig(
             enable_padding=True,